smids_5x_deit_tiny_sgd_00001_fold5
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0692
- Accuracy: 0.45
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4013 | 1.0 | 375 | 1.3267 | 0.3533 |
| 1.3461 | 2.0 | 750 | 1.2947 | 0.36 |
| 1.2972 | 3.0 | 1125 | 1.2670 | 0.3617 |
| 1.31 | 4.0 | 1500 | 1.2433 | 0.3683 |
| 1.2221 | 5.0 | 1875 | 1.2236 | 0.3717 |
| 1.2656 | 6.0 | 2250 | 1.2067 | 0.375 |
| 1.2312 | 7.0 | 2625 | 1.1923 | 0.3817 |
| 1.1861 | 8.0 | 3000 | 1.1803 | 0.3817 |
| 1.1289 | 9.0 | 3375 | 1.1699 | 0.385 |
| 1.218 | 10.0 | 3750 | 1.1612 | 0.3867 |
| 1.1921 | 11.0 | 4125 | 1.1535 | 0.3983 |
| 1.1315 | 12.0 | 4500 | 1.1468 | 0.405 |
| 1.1732 | 13.0 | 4875 | 1.1407 | 0.4133 |
| 1.1412 | 14.0 | 5250 | 1.1354 | 0.41 |
| 1.1502 | 15.0 | 5625 | 1.1305 | 0.4133 |
| 1.126 | 16.0 | 6000 | 1.1259 | 0.4133 |
| 1.1098 | 17.0 | 6375 | 1.1217 | 0.4117 |
| 1.1197 | 18.0 | 6750 | 1.1177 | 0.4083 |
| 1.1329 | 19.0 | 7125 | 1.1140 | 0.4083 |
| 1.0741 | 20.0 | 7500 | 1.1105 | 0.4117 |
| 1.0617 | 21.0 | 7875 | 1.1072 | 0.4117 |
| 1.0917 | 22.0 | 8250 | 1.1041 | 0.41 |
| 1.0822 | 23.0 | 8625 | 1.1011 | 0.41 |
| 1.1336 | 24.0 | 9000 | 1.0984 | 0.4167 |
| 1.0665 | 25.0 | 9375 | 1.0958 | 0.415 |
| 1.1097 | 26.0 | 9750 | 1.0934 | 0.415 |
| 1.0499 | 27.0 | 10125 | 1.0911 | 0.4183 |
| 1.1202 | 28.0 | 10500 | 1.0889 | 0.4167 |
| 1.1038 | 29.0 | 10875 | 1.0869 | 0.4283 |
| 1.0838 | 30.0 | 11250 | 1.0850 | 0.4333 |
| 1.0717 | 31.0 | 11625 | 1.0832 | 0.4367 |
| 1.0773 | 32.0 | 12000 | 1.0816 | 0.4383 |
| 1.0858 | 33.0 | 12375 | 1.0800 | 0.44 |
| 1.0072 | 34.0 | 12750 | 1.0786 | 0.44 |
| 1.0435 | 35.0 | 13125 | 1.0773 | 0.4417 |
| 1.047 | 36.0 | 13500 | 1.0761 | 0.4433 |
| 1.0361 | 37.0 | 13875 | 1.0750 | 0.4483 |
| 1.0477 | 38.0 | 14250 | 1.0740 | 0.45 |
| 1.0658 | 39.0 | 14625 | 1.0731 | 0.4483 |
| 1.0711 | 40.0 | 15000 | 1.0723 | 0.445 |
| 1.0473 | 41.0 | 15375 | 1.0716 | 0.445 |
| 1.0521 | 42.0 | 15750 | 1.0711 | 0.445 |
| 1.0368 | 43.0 | 16125 | 1.0705 | 0.4467 |
| 1.0636 | 44.0 | 16500 | 1.0701 | 0.4483 |
| 1.0424 | 45.0 | 16875 | 1.0698 | 0.4483 |
| 1.0442 | 46.0 | 17250 | 1.0695 | 0.45 |
| 1.0667 | 47.0 | 17625 | 1.0694 | 0.45 |
| 1.0523 | 48.0 | 18000 | 1.0693 | 0.45 |
| 1.0135 | 49.0 | 18375 | 1.0692 | 0.45 |
| 1.0393 | 50.0 | 18750 | 1.0692 | 0.45 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2